






"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""image_processor""", """tokenizer"""]
    __UpperCAmelCase           = """AutoImageProcessor"""
    __UpperCAmelCase           = """AutoTokenizer"""







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	List[Any]=None				,    UpperCamelCase_ :	Tuple=None				,    **UpperCamelCase_ :	int ):





           '''simple docstring'''

           __magic_name__      				=    None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                      ' instead.'				,    UpperCamelCase_				,    )
                  __magic_name__      				=    kwargs.pop('feature_extractor' )

           __magic_name__      				=    image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError('You need to specify an `image_processor`.' )
           if tokenizer is None:
                  raise ValueError('You need to specify a `tokenizer`.' )

           super().__init__(UpperCamelCase_				,    UpperCamelCase_ )
           __magic_name__      				=    self.image_processor
           __magic_name__      				=    False







    def __call__(		self :	List[str]				,    *UpperCamelCase_ :	str				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           if self._in_target_context_manager:
                  return self.current_processor(*UpperCamelCase_				,    **UpperCamelCase_ )

           __magic_name__      				=    kwargs.pop('images'				,    UpperCamelCase_ )
           __magic_name__      				=    kwargs.pop('text'				,    UpperCamelCase_ )
           if len(UpperCamelCase_ ) > 0:
                  __magic_name__      				=    args[0]
                  __magic_name__      				=    args[1:]

           if images is None and text is None:
                  raise ValueError('You need to specify either an `images` or `text` input to process.' )

           if images is not None:
                  __magic_name__      				=    self.image_processor(UpperCamelCase_				,    *UpperCamelCase_				,    **UpperCamelCase_ )
           if text is not None:
                  __magic_name__      				=    self.tokenizer(UpperCamelCase_				,    **UpperCamelCase_ )

           if text is None:
                  return inputs
           elif images is None:
                  return encodings
           else:
                  __magic_name__      				=    encodings['input_ids']
                  return inputs







    def 				a__			(		self :	str				,    *UpperCamelCase_ :	Dict				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           return self.tokenizer.batch_decode(*UpperCamelCase_				,    **UpperCamelCase_ )







    def 				a__			(		self :	Any				,    *UpperCamelCase_ :	str				,    **UpperCamelCase_ :	int ):





           '''simple docstring'''

           return self.tokenizer.decode(*UpperCamelCase_				,    **UpperCamelCase_ )







    @contextmanager
    def 				a__			(		self :	int ):





           '''simple docstring'''

           warnings.warn(
               '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
               'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
               'your images inputs, or in a separate call.' )
           __magic_name__      				=    True
           __magic_name__      				=    self.tokenizer
           yield
           __magic_name__      				=    self.image_processor
           __magic_name__      				=    False







    def 				a__			(		self :	List[str]				,    UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Optional[Any]=False				,    UpperCamelCase_ :	Optional[int]=None ):





           '''simple docstring'''

           if added_vocab is None:
                  __magic_name__      				=    self.tokenizer.get_added_vocab()

           __magic_name__      				=    {}

           while tokens:
                  __magic_name__      				=    re.search(r'<s_(.*?)>'				,    UpperCamelCase_				,    re.IGNORECASE )
                  if start_token is None:
                         break
                  __magic_name__      				=    start_token.group(1 )
                  __magic_name__      				=    re.search(rf"""</s_{key}>"""				,    UpperCamelCase_				,    re.IGNORECASE )
                  __magic_name__      				=    start_token.group()
                  if end_token is None:
                         __magic_name__      				=    tokens.replace(UpperCamelCase_				,    '' )
                  else:
                         __magic_name__      				=    end_token.group()
                         __magic_name__      				=    re.escape(UpperCamelCase_ )
                         __magic_name__      				=    re.escape(UpperCamelCase_ )
                         __magic_name__      				=    re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""				,    UpperCamelCase_				,    re.IGNORECASE )
                         if content is not None:
                                __magic_name__      				=    content.group(1 ).strip()
                                if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                       __magic_name__      				=    self.tokenajson(UpperCamelCase_				,    is_inner_value=UpperCamelCase_				,    added_vocab=UpperCamelCase_ )
                                       if value:
                                              if len(UpperCamelCase_ ) == 1:
                                                     __magic_name__      				=    value[0]
                                              __magic_name__      				=    value
                                else:  # leaf nodes
                                       __magic_name__      				=    []
                                       for leaf in content.split(r'<sep/>' ):
                                              __magic_name__      				=    leaf.strip()
                                              if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                     __magic_name__      				=    leaf[1:-2]  # for categorical special tokens
                                              output[key].append(UpperCamelCase_ )
                                       if len(output[key] ) == 1:
                                              __magic_name__      				=    output[key][0]

                         __magic_name__      				=    tokens[tokens.find(UpperCamelCase_ ) + len(UpperCamelCase_ ) :].strip()
                         if tokens[:6] == r"<sep/>":  # non-leaf nodes
                                return [output] + self.tokenajson(tokens[6:]				,    is_inner_value=UpperCamelCase_				,    added_vocab=UpperCamelCase_ )

           if len(UpperCamelCase_ ):
                  return [output] if is_inner_value else output
           else:
                  return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           warnings.warn(
               '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,    UpperCamelCase_				,    )
           return self.image_processor_class







    @property
    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           warnings.warn(
               '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,    UpperCamelCase_				,    )
           return self.image_processor







"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 							A						(				__snake_case: Optional[int]  )    ->		Tuple:

       """simple docstring"""





       for param in module.parameters():
              __magic_name__      				=    False


def 							A						(				)    ->		Optional[Any]:

       """simple docstring"""





       __magic_name__      				=    'cuda' if torch.cuda.is_available() else 'cpu'
       if torch.backends.mps.is_available() and torch.backends.mps.is_built():
              __magic_name__      				=    'mps'
       if device == "mps":
              print(
                  'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
                  ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
                  ' with generations.'  )
       return device


def 							A						(				__snake_case: int  )    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    plt.imshow(__snake_case  )
       fig.axes.get_xaxis().set_visible(__snake_case  )
       fig.axes.get_yaxis().set_visible(__snake_case  )
       plt.show()






def 							A						(				)    ->		List[Any]:

       """simple docstring"""





       __magic_name__      				=    datetime.now()
       __magic_name__      				=    current_time.strftime('%H:%M:%S'  )
       return timestamp